Optimization Model for Crop Planting Scheme Based on Mixed-Integer Programming

Jingwen Yan; Kai Wu; Haonan Wang

doi:10.54097/px6mcv21

Authors

Jingwen Yan
Kai Wu
Haonan Wang

DOI:

https://doi.org/10.54097/px6mcv21

Keywords:

Mixed-Integer Programming Model, Crop Planting Optimization, Sustainable Agricultural Development, Robust Optimization Methods.

Abstract

In response to the rural revitalization strategy, this paper analyzes and models relevant data based on the crop planting optimization model, aiming to formulate the optimal crop planting scheme for a village in North China. Firstly, data fitting and visualization were carried out, including creating bar charts, frequency distribution histograms, and normal fitting curves of crop yields. Next, the mixed integer programming model was used to optimize and solve the maximum profit and total profit from 2024 to 2030 in the case of overproduction and unsalable surplus. In addition, a sensitivity analysis of data perturbation was conducted for the planting cost to explore the impact of changes in the planting cost on profit, demonstrating that the model has good robustness and stability. This indicates that even when the planting cost is uncertain, the model can maintain stable profit predictions, thus providing effective decision support for crop planting.

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References

[1] Lin Bingkun, Guo Guoqing. The Development Mechanism of Creative Agriculture Driven by the Rural Revitalization Strategy [J]. Journal of Shanxi University of Finance and Economics, 2024, 46(12): 80-93.

[2] Sun Kun, Xia Zhaogang, Luan Zhihua, et al. Countermeasures and Suggestions for the Development of Organic Agriculture under the Comprehensive Rural Revitalization Strategy [J]. Quality and Safety of Agricultural Products, 2024, (05): 94-98.

[3] Tian Huamin. Research on Green Agricultural Technology Innovation and Sustainable Development Strategy [J]. Intelligent Agricultural Guide, 2024, 4(22): 78-81.

[4] Wei Yan. The Influence of Environmental Factors in Agricultural Planting and Cultivation and the Countermeasures [J]. Hebei Agricultural Machinery, 2024, (17): 136-138.

[5] Xue Yi. The History and Development of Linear Programming [J]. Mathematical Modeling and Its Applications, 2024, 13(03): 100-105.

[6] Zhang Huawei. The Combined Effect of Biochar and Mycorrhiza on Reducing the Absorption of Heavy Metal Cadmium by Maize [J]. Journal of Changzhi University, 2022, 39(02): 41-47.

[7] Jia Haigang, Wu Jianling. Research on the Internal Logic, Influencing Factors and Stable Strategies of Price Game in the Vegetable Supply Chain - An Investigation and Analysis of the Vegetable Market Supply Chain in Chengdu Based on the Process Tracing Method [J/OL]. Chinese Journal of Agricultural Resources and Regional Planning, 1 - 16 [2024 - 11 - 21].

[8] Ioan D, Prodan I, Olaru S, et al. Mixed-integer programming in motion planning[J]. Annual Reviews in Control, 2021, 51: 65-87.

[9] Sun Kaiheng. Research on Robust Optimization Model of Supply Chain Scheduling under Uncertain Delivery Time [D]. Dalian Maritime University, 2022.

[10] Wang Yuyan, Yu Zhaoqing. Multi-agent E-supply Chain Decision-making Considering Product Substitutability and Complementarity [J]. Management Review, 2020, 32(05): 255-268.

[11] Gui Yumin. Research on Enterprise Profit Forecasting and Analysis Based on the Profit Sensitivity Analysis Model [J]. Knowledge Economy, 2020, (07): 78 + 80.